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<?php |
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declare(strict_types=1); |
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/** |
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* |
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* Class to obtain eigenvalues and eigenvectors of a real matrix. |
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* |
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* If A is symmetric, then A = V*D*V' where the eigenvalue matrix D |
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* is diagonal and the eigenvector matrix V is orthogonal (i.e. |
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* A = V.times(D.times(V.transpose())) and V.times(V.transpose()) |
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* equals the identity matrix). |
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* |
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* If A is not symmetric, then the eigenvalue matrix D is block diagonal |
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* with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues, |
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* lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda]. The |
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* columns of V represent the eigenvectors in the sense that A*V = V*D, |
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* i.e. A.times(V) equals V.times(D). The matrix V may be badly |
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* conditioned, or even singular, so the validity of the equation |
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* A = V*D*inverse(V) depends upon V.cond(). |
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* |
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* @author Paul Meagher |
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* @license PHP v3.0 |
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* @version 1.1 |
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* |
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* Slightly changed to adapt the original code to PHP-ML library |
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* @date 2017/04/11 |
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* @author Mustafa Karabulut |
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*/ |
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namespace Phpml\Math\LinearAlgebra; |
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use Phpml\Math\Matrix; |
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class EigenvalueDecomposition |
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{ |
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/** |
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* Row and column dimension (square matrix). |
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* @var int |
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*/ |
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private $n; |
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/** |
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* Internal symmetry flag. |
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* @var int |
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*/ |
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private $issymmetric; |
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/** |
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* Arrays for internal storage of eigenvalues. |
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* @var array |
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*/ |
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private $d = []; |
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private $e = []; |
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/** |
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* Array for internal storage of eigenvectors. |
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* @var array |
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*/ |
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private $V = []; |
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/** |
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* Array for internal storage of nonsymmetric Hessenberg form. |
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* @var array |
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*/ |
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private $H = []; |
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/** |
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* Working storage for nonsymmetric algorithm. |
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* @var array |
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*/ |
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private $ort; |
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/** |
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* Used for complex scalar division. |
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* @var float |
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*/ |
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private $cdivr; |
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private $cdivi; |
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/** |
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* Symmetric Householder reduction to tridiagonal form. |
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*/ |
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private function tred2() |
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{ |
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// This is derived from the Algol procedures tred2 by |
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// Bowdler, Martin, Reinsch, and Wilkinson, Handbook for |
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// Auto. Comp., Vol.ii-Linear Algebra, and the corresponding |
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// Fortran subroutine in EISPACK. |
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$this->d = $this->V[$this->n-1]; |
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// Householder reduction to tridiagonal form. |
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for ($i = $this->n-1; $i > 0; --$i) { |
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$i_ = $i -1; |
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// Scale to avoid under/overflow. |
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$h = $scale = 0.0; |
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$scale += array_sum(array_map('abs', $this->d)); |
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if ($scale == 0.0) { |
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$this->e[$i] = $this->d[$i_]; |
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$this->d = array_slice($this->V[$i_], 0, $i_); |
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for ($j = 0; $j < $i; ++$j) { |
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$this->V[$j][$i] = $this->V[$i][$j] = 0.0; |
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} |
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} else { |
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// Generate Householder vector. |
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for ($k = 0; $k < $i; ++$k) { |
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$this->d[$k] /= $scale; |
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$h += pow($this->d[$k], 2); |
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} |
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$f = $this->d[$i_]; |
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$g = sqrt($h); |
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if ($f > 0) { |
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$g = -$g; |
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} |
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$this->e[$i] = $scale * $g; |
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$h = $h - $f * $g; |
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$this->d[$i_] = $f - $g; |
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for ($j = 0; $j < $i; ++$j) { |
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$this->e[$j] = 0.0; |
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} |
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// Apply similarity transformation to remaining columns. |
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for ($j = 0; $j < $i; ++$j) { |
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$f = $this->d[$j]; |
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$this->V[$j][$i] = $f; |
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$g = $this->e[$j] + $this->V[$j][$j] * $f; |
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for ($k = $j+1; $k <= $i_; ++$k) { |
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$g += $this->V[$k][$j] * $this->d[$k]; |
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$this->e[$k] += $this->V[$k][$j] * $f; |
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} |
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$this->e[$j] = $g; |
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} |
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$f = 0.0; |
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for ($j = 0; $j < $i; ++$j) { |
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if ($h === 0) { |
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$h = 1e-20; |
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} |
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$this->e[$j] /= $h; |
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$f += $this->e[$j] * $this->d[$j]; |
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} |
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$hh = $f / (2 * $h); |
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for ($j=0; $j < $i; ++$j) { |
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$this->e[$j] -= $hh * $this->d[$j]; |
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} |
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for ($j = 0; $j < $i; ++$j) { |
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$f = $this->d[$j]; |
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$g = $this->e[$j]; |
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for ($k = $j; $k <= $i_; ++$k) { |
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$this->V[$k][$j] -= ($f * $this->e[$k] + $g * $this->d[$k]); |
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} |
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$this->d[$j] = $this->V[$i-1][$j]; |
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$this->V[$i][$j] = 0.0; |
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} |
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} |
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$this->d[$i] = $h; |
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} |
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// Accumulate transformations. |
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for ($i = 0; $i < $this->n-1; ++$i) { |
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$this->V[$this->n-1][$i] = $this->V[$i][$i]; |
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$this->V[$i][$i] = 1.0; |
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$h = $this->d[$i+1]; |
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if ($h != 0.0) { |
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View Code Duplication |
for ($k = 0; $k <= $i; ++$k) { |
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$this->d[$k] = $this->V[$k][$i+1] / $h; |
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} |
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for ($j = 0; $j <= $i; ++$j) { |
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$g = 0.0; |
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View Code Duplication |
for ($k = 0; $k <= $i; ++$k) { |
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$g += $this->V[$k][$i+1] * $this->V[$k][$j]; |
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} |
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View Code Duplication |
for ($k = 0; $k <= $i; ++$k) { |
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$this->V[$k][$j] -= $g * $this->d[$k]; |
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} |
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} |
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} |
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for ($k = 0; $k <= $i; ++$k) { |
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$this->V[$k][$i+1] = 0.0; |
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} |
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} |
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$this->d = $this->V[$this->n-1]; |
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$this->V[$this->n-1] = array_fill(0, $j, 0.0); |
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$this->V[$this->n-1][$this->n-1] = 1.0; |
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$this->e[0] = 0.0; |
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} |
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/** |
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* Symmetric tridiagonal QL algorithm. |
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* |
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* This is derived from the Algol procedures tql2, by |
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* Bowdler, Martin, Reinsch, and Wilkinson, Handbook for |
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* Auto. Comp., Vol.ii-Linear Algebra, and the corresponding |
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* Fortran subroutine in EISPACK. |
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*/ |
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private function tql2() |
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{ |
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for ($i = 1; $i < $this->n; ++$i) { |
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$this->e[$i-1] = $this->e[$i]; |
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} |
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$this->e[$this->n-1] = 0.0; |
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$f = 0.0; |
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$tst1 = 0.0; |
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$eps = pow(2.0, -52.0); |
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for ($l = 0; $l < $this->n; ++$l) { |
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// Find small subdiagonal element |
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$tst1 = max($tst1, abs($this->d[$l]) + abs($this->e[$l])); |
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$m = $l; |
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while ($m < $this->n) { |
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if (abs($this->e[$m]) <= $eps * $tst1) { |
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break; |
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} |
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++$m; |
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} |
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// If m == l, $this->d[l] is an eigenvalue, |
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// otherwise, iterate. |
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if ($m > $l) { |
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$iter = 0; |
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do { |
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// Could check iteration count here. |
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$iter += 1; |
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// Compute implicit shift |
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$g = $this->d[$l]; |
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$p = ($this->d[$l+1] - $g) / (2.0 * $this->e[$l]); |
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$r = hypot($p, 1.0); |
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if ($p < 0) { |
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$r *= -1; |
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} |
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$this->d[$l] = $this->e[$l] / ($p + $r); |
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$this->d[$l+1] = $this->e[$l] * ($p + $r); |
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$dl1 = $this->d[$l+1]; |
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$h = $g - $this->d[$l]; |
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for ($i = $l + 2; $i < $this->n; ++$i) { |
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$this->d[$i] -= $h; |
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} |
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$f += $h; |
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// Implicit QL transformation. |
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$p = $this->d[$m]; |
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$c = 1.0; |
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$c2 = $c3 = $c; |
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$el1 = $this->e[$l + 1]; |
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$s = $s2 = 0.0; |
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for ($i = $m-1; $i >= $l; --$i) { |
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$c3 = $c2; |
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$c2 = $c; |
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$s2 = $s; |
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$g = $c * $this->e[$i]; |
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$h = $c * $p; |
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$r = hypot($p, $this->e[$i]); |
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$this->e[$i+1] = $s * $r; |
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$s = $this->e[$i] / $r; |
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$c = $p / $r; |
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$p = $c * $this->d[$i] - $s * $g; |
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$this->d[$i+1] = $h + $s * ($c * $g + $s * $this->d[$i]); |
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// Accumulate transformation. |
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for ($k = 0; $k < $this->n; ++$k) { |
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$h = $this->V[$k][$i+1]; |
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$this->V[$k][$i+1] = $s * $this->V[$k][$i] + $c * $h; |
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$this->V[$k][$i] = $c * $this->V[$k][$i] - $s * $h; |
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} |
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} |
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$p = -$s * $s2 * $c3 * $el1 * $this->e[$l] / $dl1; |
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$this->e[$l] = $s * $p; |
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$this->d[$l] = $c * $p; |
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// Check for convergence. |
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} while (abs($this->e[$l]) > $eps * $tst1); |
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} |
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$this->d[$l] = $this->d[$l] + $f; |
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$this->e[$l] = 0.0; |
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} |
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// Sort eigenvalues and corresponding vectors. |
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for ($i = 0; $i < $this->n - 1; ++$i) { |
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$k = $i; |
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$p = $this->d[$i]; |
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for ($j = $i+1; $j < $this->n; ++$j) { |
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if ($this->d[$j] < $p) { |
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$k = $j; |
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$p = $this->d[$j]; |
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} |
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} |
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if ($k != $i) { |
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$this->d[$k] = $this->d[$i]; |
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$this->d[$i] = $p; |
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for ($j = 0; $j < $this->n; ++$j) { |
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$p = $this->V[$j][$i]; |
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$this->V[$j][$i] = $this->V[$j][$k]; |
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$this->V[$j][$k] = $p; |
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} |
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} |
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} |
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} |
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295
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296
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/** |
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297
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* Nonsymmetric reduction to Hessenberg form. |
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298
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* |
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299
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* This is derived from the Algol procedures orthes and ortran, |
|
300
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* by Martin and Wilkinson, Handbook for Auto. Comp., |
|
301
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* Vol.ii-Linear Algebra, and the corresponding |
|
302
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* Fortran subroutines in EISPACK. |
|
303
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|
*/ |
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304
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|
private function orthes() |
|
305
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|
|
{ |
|
306
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|
|
$low = 0; |
|
307
|
|
|
$high = $this->n-1; |
|
308
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|
|
309
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for ($m = $low+1; $m <= $high-1; ++$m) { |
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310
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// Scale column. |
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311
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$scale = 0.0; |
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312
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View Code Duplication |
for ($i = $m; $i <= $high; ++$i) { |
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|
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|
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313
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$scale = $scale + abs($this->H[$i][$m-1]); |
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314
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} |
|
315
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|
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if ($scale != 0.0) { |
|
316
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// Compute Householder transformation. |
|
317
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$h = 0.0; |
|
318
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for ($i = $high; $i >= $m; --$i) { |
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319
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$this->ort[$i] = $this->H[$i][$m-1] / $scale; |
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320
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$h += $this->ort[$i] * $this->ort[$i]; |
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321
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} |
|
322
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$g = sqrt($h); |
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323
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if ($this->ort[$m] > 0) { |
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324
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$g *= -1; |
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325
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} |
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326
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$h -= $this->ort[$m] * $g; |
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327
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$this->ort[$m] -= $g; |
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328
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// Apply Householder similarity transformation |
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329
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// H = (I -u * u' / h) * H * (I -u * u') / h) |
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330
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View Code Duplication |
for ($j = $m; $j < $this->n; ++$j) { |
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331
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$f = 0.0; |
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332
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for ($i = $high; $i >= $m; --$i) { |
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333
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$f += $this->ort[$i] * $this->H[$i][$j]; |
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334
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} |
|
335
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$f /= $h; |
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336
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for ($i = $m; $i <= $high; ++$i) { |
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337
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$this->H[$i][$j] -= $f * $this->ort[$i]; |
|
338
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} |
|
339
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} |
|
340
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|
View Code Duplication |
for ($i = 0; $i <= $high; ++$i) { |
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|
341
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$f = 0.0; |
|
342
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|
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for ($j = $high; $j >= $m; --$j) { |
|
343
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$f += $this->ort[$j] * $this->H[$i][$j]; |
|
344
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} |
|
345
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|
$f = $f / $h; |
|
346
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|
for ($j = $m; $j <= $high; ++$j) { |
|
347
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$this->H[$i][$j] -= $f * $this->ort[$j]; |
|
348
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|
|
} |
|
349
|
|
|
} |
|
350
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|
$this->ort[$m] = $scale * $this->ort[$m]; |
|
351
|
|
|
$this->H[$m][$m-1] = $scale * $g; |
|
352
|
|
|
} |
|
353
|
|
|
} |
|
354
|
|
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|
|
355
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|
|
// Accumulate transformations (Algol's ortran). |
|
356
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|
for ($i = 0; $i < $this->n; ++$i) { |
|
357
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|
|
for ($j = 0; $j < $this->n; ++$j) { |
|
358
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|
$this->V[$i][$j] = ($i == $j ? 1.0 : 0.0); |
|
359
|
|
|
} |
|
360
|
|
|
} |
|
361
|
|
|
for ($m = $high-1; $m >= $low+1; --$m) { |
|
362
|
|
|
if ($this->H[$m][$m-1] != 0.0) { |
|
363
|
|
View Code Duplication |
for ($i = $m+1; $i <= $high; ++$i) { |
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|
|
|
|
|
364
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|
|
$this->ort[$i] = $this->H[$i][$m-1]; |
|
365
|
|
|
} |
|
366
|
|
|
for ($j = $m; $j <= $high; ++$j) { |
|
367
|
|
|
$g = 0.0; |
|
368
|
|
|
for ($i = $m; $i <= $high; ++$i) { |
|
369
|
|
|
$g += $this->ort[$i] * $this->V[$i][$j]; |
|
370
|
|
|
} |
|
371
|
|
|
// Double division avoids possible underflow |
|
372
|
|
|
$g = ($g / $this->ort[$m]) / $this->H[$m][$m-1]; |
|
373
|
|
|
for ($i = $m; $i <= $high; ++$i) { |
|
374
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|
|
$this->V[$i][$j] += $g * $this->ort[$i]; |
|
375
|
|
|
} |
|
376
|
|
|
} |
|
377
|
|
|
} |
|
378
|
|
|
} |
|
379
|
|
|
} |
|
380
|
|
|
|
|
381
|
|
|
|
|
382
|
|
|
/** |
|
383
|
|
|
* Performs complex division. |
|
384
|
|
|
*/ |
|
385
|
|
|
private function cdiv($xr, $xi, $yr, $yi) |
|
386
|
|
|
{ |
|
387
|
|
|
if (abs($yr) > abs($yi)) { |
|
388
|
|
|
$r = $yi / $yr; |
|
389
|
|
|
$d = $yr + $r * $yi; |
|
390
|
|
|
$this->cdivr = ($xr + $r * $xi) / $d; |
|
391
|
|
|
$this->cdivi = ($xi - $r * $xr) / $d; |
|
392
|
|
|
} else { |
|
393
|
|
|
$r = $yr / $yi; |
|
394
|
|
|
$d = $yi + $r * $yr; |
|
395
|
|
|
$this->cdivr = ($r * $xr + $xi) / $d; |
|
396
|
|
|
$this->cdivi = ($r * $xi - $xr) / $d; |
|
397
|
|
|
} |
|
398
|
|
|
} |
|
399
|
|
|
|
|
400
|
|
|
|
|
401
|
|
|
/** |
|
402
|
|
|
* Nonsymmetric reduction from Hessenberg to real Schur form. |
|
403
|
|
|
* |
|
404
|
|
|
* Code is derived from the Algol procedure hqr2, |
|
405
|
|
|
* by Martin and Wilkinson, Handbook for Auto. Comp., |
|
406
|
|
|
* Vol.ii-Linear Algebra, and the corresponding |
|
407
|
|
|
* Fortran subroutine in EISPACK. |
|
408
|
|
|
*/ |
|
409
|
|
|
private function hqr2() |
|
410
|
|
|
{ |
|
411
|
|
|
// Initialize |
|
412
|
|
|
$nn = $this->n; |
|
413
|
|
|
$n = $nn - 1; |
|
414
|
|
|
$low = 0; |
|
415
|
|
|
$high = $nn - 1; |
|
416
|
|
|
$eps = pow(2.0, -52.0); |
|
417
|
|
|
$exshift = 0.0; |
|
418
|
|
|
$p = $q = $r = $s = $z = 0; |
|
419
|
|
|
// Store roots isolated by balanc and compute matrix norm |
|
420
|
|
|
$norm = 0.0; |
|
421
|
|
|
|
|
422
|
|
|
for ($i = 0; $i < $nn; ++$i) { |
|
423
|
|
|
if (($i < $low) or ($i > $high)) { |
|
|
|
|
|
|
424
|
|
|
$this->d[$i] = $this->H[$i][$i]; |
|
425
|
|
|
$this->e[$i] = 0.0; |
|
426
|
|
|
} |
|
427
|
|
|
for ($j = max($i-1, 0); $j < $nn; ++$j) { |
|
428
|
|
|
$norm = $norm + abs($this->H[$i][$j]); |
|
429
|
|
|
} |
|
430
|
|
|
} |
|
431
|
|
|
|
|
432
|
|
|
// Outer loop over eigenvalue index |
|
433
|
|
|
$iter = 0; |
|
434
|
|
|
while ($n >= $low) { |
|
435
|
|
|
// Look for single small sub-diagonal element |
|
436
|
|
|
$l = $n; |
|
437
|
|
|
while ($l > $low) { |
|
438
|
|
|
$s = abs($this->H[$l-1][$l-1]) + abs($this->H[$l][$l]); |
|
439
|
|
|
if ($s == 0.0) { |
|
440
|
|
|
$s = $norm; |
|
441
|
|
|
} |
|
442
|
|
|
if (abs($this->H[$l][$l-1]) < $eps * $s) { |
|
443
|
|
|
break; |
|
444
|
|
|
} |
|
445
|
|
|
--$l; |
|
446
|
|
|
} |
|
447
|
|
|
// Check for convergence |
|
448
|
|
|
// One root found |
|
449
|
|
|
if ($l == $n) { |
|
450
|
|
|
$this->H[$n][$n] = $this->H[$n][$n] + $exshift; |
|
451
|
|
|
$this->d[$n] = $this->H[$n][$n]; |
|
452
|
|
|
$this->e[$n] = 0.0; |
|
453
|
|
|
--$n; |
|
454
|
|
|
$iter = 0; |
|
455
|
|
|
// Two roots found |
|
456
|
|
|
} elseif ($l == $n-1) { |
|
457
|
|
|
$w = $this->H[$n][$n-1] * $this->H[$n-1][$n]; |
|
458
|
|
|
$p = ($this->H[$n-1][$n-1] - $this->H[$n][$n]) / 2.0; |
|
459
|
|
|
$q = $p * $p + $w; |
|
460
|
|
|
$z = sqrt(abs($q)); |
|
461
|
|
|
$this->H[$n][$n] = $this->H[$n][$n] + $exshift; |
|
462
|
|
|
$this->H[$n-1][$n-1] = $this->H[$n-1][$n-1] + $exshift; |
|
463
|
|
|
$x = $this->H[$n][$n]; |
|
464
|
|
|
// Real pair |
|
465
|
|
|
if ($q >= 0) { |
|
466
|
|
|
if ($p >= 0) { |
|
467
|
|
|
$z = $p + $z; |
|
468
|
|
|
} else { |
|
469
|
|
|
$z = $p - $z; |
|
470
|
|
|
} |
|
471
|
|
|
$this->d[$n-1] = $x + $z; |
|
472
|
|
|
$this->d[$n] = $this->d[$n-1]; |
|
473
|
|
|
if ($z != 0.0) { |
|
474
|
|
|
$this->d[$n] = $x - $w / $z; |
|
475
|
|
|
} |
|
476
|
|
|
$this->e[$n-1] = 0.0; |
|
477
|
|
|
$this->e[$n] = 0.0; |
|
478
|
|
|
$x = $this->H[$n][$n-1]; |
|
479
|
|
|
$s = abs($x) + abs($z); |
|
480
|
|
|
$p = $x / $s; |
|
481
|
|
|
$q = $z / $s; |
|
482
|
|
|
$r = sqrt($p * $p + $q * $q); |
|
483
|
|
|
$p = $p / $r; |
|
484
|
|
|
$q = $q / $r; |
|
485
|
|
|
// Row modification |
|
486
|
|
View Code Duplication |
for ($j = $n-1; $j < $nn; ++$j) { |
|
|
|
|
|
|
487
|
|
|
$z = $this->H[$n-1][$j]; |
|
488
|
|
|
$this->H[$n-1][$j] = $q * $z + $p * $this->H[$n][$j]; |
|
489
|
|
|
$this->H[$n][$j] = $q * $this->H[$n][$j] - $p * $z; |
|
490
|
|
|
} |
|
491
|
|
|
// Column modification |
|
492
|
|
View Code Duplication |
for ($i = 0; $i <= $n; ++$i) { |
|
|
|
|
|
|
493
|
|
|
$z = $this->H[$i][$n-1]; |
|
494
|
|
|
$this->H[$i][$n-1] = $q * $z + $p * $this->H[$i][$n]; |
|
495
|
|
|
$this->H[$i][$n] = $q * $this->H[$i][$n] - $p * $z; |
|
496
|
|
|
} |
|
497
|
|
|
// Accumulate transformations |
|
498
|
|
View Code Duplication |
for ($i = $low; $i <= $high; ++$i) { |
|
|
|
|
|
|
499
|
|
|
$z = $this->V[$i][$n-1]; |
|
500
|
|
|
$this->V[$i][$n-1] = $q * $z + $p * $this->V[$i][$n]; |
|
501
|
|
|
$this->V[$i][$n] = $q * $this->V[$i][$n] - $p * $z; |
|
502
|
|
|
} |
|
503
|
|
|
// Complex pair |
|
504
|
|
|
} else { |
|
505
|
|
|
$this->d[$n-1] = $x + $p; |
|
506
|
|
|
$this->d[$n] = $x + $p; |
|
507
|
|
|
$this->e[$n-1] = $z; |
|
508
|
|
|
$this->e[$n] = -$z; |
|
509
|
|
|
} |
|
510
|
|
|
$n = $n - 2; |
|
511
|
|
|
$iter = 0; |
|
512
|
|
|
// No convergence yet |
|
513
|
|
|
} else { |
|
514
|
|
|
// Form shift |
|
515
|
|
|
$x = $this->H[$n][$n]; |
|
516
|
|
|
$y = 0.0; |
|
517
|
|
|
$w = 0.0; |
|
518
|
|
|
if ($l < $n) { |
|
519
|
|
|
$y = $this->H[$n-1][$n-1]; |
|
520
|
|
|
$w = $this->H[$n][$n-1] * $this->H[$n-1][$n]; |
|
521
|
|
|
} |
|
522
|
|
|
// Wilkinson's original ad hoc shift |
|
523
|
|
|
if ($iter == 10) { |
|
524
|
|
|
$exshift += $x; |
|
525
|
|
|
for ($i = $low; $i <= $n; ++$i) { |
|
526
|
|
|
$this->H[$i][$i] -= $x; |
|
527
|
|
|
} |
|
528
|
|
|
$s = abs($this->H[$n][$n-1]) + abs($this->H[$n-1][$n-2]); |
|
529
|
|
|
$x = $y = 0.75 * $s; |
|
530
|
|
|
$w = -0.4375 * $s * $s; |
|
531
|
|
|
} |
|
532
|
|
|
// MATLAB's new ad hoc shift |
|
533
|
|
|
if ($iter == 30) { |
|
534
|
|
|
$s = ($y - $x) / 2.0; |
|
535
|
|
|
$s = $s * $s + $w; |
|
536
|
|
|
if ($s > 0) { |
|
537
|
|
|
$s = sqrt($s); |
|
538
|
|
|
if ($y < $x) { |
|
539
|
|
|
$s = -$s; |
|
540
|
|
|
} |
|
541
|
|
|
$s = $x - $w / (($y - $x) / 2.0 + $s); |
|
542
|
|
|
for ($i = $low; $i <= $n; ++$i) { |
|
543
|
|
|
$this->H[$i][$i] -= $s; |
|
544
|
|
|
} |
|
545
|
|
|
$exshift += $s; |
|
546
|
|
|
$x = $y = $w = 0.964; |
|
547
|
|
|
} |
|
548
|
|
|
} |
|
549
|
|
|
// Could check iteration count here. |
|
550
|
|
|
$iter = $iter + 1; |
|
551
|
|
|
// Look for two consecutive small sub-diagonal elements |
|
552
|
|
|
$m = $n - 2; |
|
553
|
|
|
while ($m >= $l) { |
|
554
|
|
|
$z = $this->H[$m][$m]; |
|
555
|
|
|
$r = $x - $z; |
|
556
|
|
|
$s = $y - $z; |
|
557
|
|
|
$p = ($r * $s - $w) / $this->H[$m+1][$m] + $this->H[$m][$m+1]; |
|
558
|
|
|
$q = $this->H[$m+1][$m+1] - $z - $r - $s; |
|
559
|
|
|
$r = $this->H[$m+2][$m+1]; |
|
560
|
|
|
$s = abs($p) + abs($q) + abs($r); |
|
561
|
|
|
$p = $p / $s; |
|
562
|
|
|
$q = $q / $s; |
|
563
|
|
|
$r = $r / $s; |
|
564
|
|
|
if ($m == $l) { |
|
565
|
|
|
break; |
|
566
|
|
|
} |
|
567
|
|
|
if (abs($this->H[$m][$m-1]) * (abs($q) + abs($r)) < |
|
568
|
|
|
$eps * (abs($p) * (abs($this->H[$m-1][$m-1]) + abs($z) + abs($this->H[$m+1][$m+1])))) { |
|
569
|
|
|
break; |
|
570
|
|
|
} |
|
571
|
|
|
--$m; |
|
572
|
|
|
} |
|
573
|
|
|
for ($i = $m + 2; $i <= $n; ++$i) { |
|
574
|
|
|
$this->H[$i][$i-2] = 0.0; |
|
575
|
|
|
if ($i > $m+2) { |
|
576
|
|
|
$this->H[$i][$i-3] = 0.0; |
|
577
|
|
|
} |
|
578
|
|
|
} |
|
579
|
|
|
// Double QR step involving rows l:n and columns m:n |
|
580
|
|
|
for ($k = $m; $k <= $n-1; ++$k) { |
|
581
|
|
|
$notlast = ($k != $n-1); |
|
582
|
|
|
if ($k != $m) { |
|
583
|
|
|
$p = $this->H[$k][$k-1]; |
|
584
|
|
|
$q = $this->H[$k+1][$k-1]; |
|
585
|
|
|
$r = ($notlast ? $this->H[$k+2][$k-1] : 0.0); |
|
586
|
|
|
$x = abs($p) + abs($q) + abs($r); |
|
587
|
|
|
if ($x != 0.0) { |
|
588
|
|
|
$p = $p / $x; |
|
589
|
|
|
$q = $q / $x; |
|
590
|
|
|
$r = $r / $x; |
|
591
|
|
|
} |
|
592
|
|
|
} |
|
593
|
|
|
if ($x == 0.0) { |
|
594
|
|
|
break; |
|
595
|
|
|
} |
|
596
|
|
|
$s = sqrt($p * $p + $q * $q + $r * $r); |
|
597
|
|
|
if ($p < 0) { |
|
598
|
|
|
$s = -$s; |
|
599
|
|
|
} |
|
600
|
|
|
if ($s != 0) { |
|
601
|
|
|
if ($k != $m) { |
|
602
|
|
|
$this->H[$k][$k-1] = -$s * $x; |
|
603
|
|
|
} elseif ($l != $m) { |
|
604
|
|
|
$this->H[$k][$k-1] = -$this->H[$k][$k-1]; |
|
605
|
|
|
} |
|
606
|
|
|
$p = $p + $s; |
|
607
|
|
|
$x = $p / $s; |
|
608
|
|
|
$y = $q / $s; |
|
609
|
|
|
$z = $r / $s; |
|
610
|
|
|
$q = $q / $p; |
|
611
|
|
|
$r = $r / $p; |
|
612
|
|
|
// Row modification |
|
613
|
|
View Code Duplication |
for ($j = $k; $j < $nn; ++$j) { |
|
|
|
|
|
|
614
|
|
|
$p = $this->H[$k][$j] + $q * $this->H[$k+1][$j]; |
|
615
|
|
|
if ($notlast) { |
|
616
|
|
|
$p = $p + $r * $this->H[$k+2][$j]; |
|
617
|
|
|
$this->H[$k+2][$j] = $this->H[$k+2][$j] - $p * $z; |
|
618
|
|
|
} |
|
619
|
|
|
$this->H[$k][$j] = $this->H[$k][$j] - $p * $x; |
|
620
|
|
|
$this->H[$k+1][$j] = $this->H[$k+1][$j] - $p * $y; |
|
621
|
|
|
} |
|
622
|
|
|
// Column modification |
|
623
|
|
View Code Duplication |
for ($i = 0; $i <= min($n, $k+3); ++$i) { |
|
|
|
|
|
|
624
|
|
|
$p = $x * $this->H[$i][$k] + $y * $this->H[$i][$k+1]; |
|
625
|
|
|
if ($notlast) { |
|
626
|
|
|
$p = $p + $z * $this->H[$i][$k+2]; |
|
627
|
|
|
$this->H[$i][$k+2] = $this->H[$i][$k+2] - $p * $r; |
|
628
|
|
|
} |
|
629
|
|
|
$this->H[$i][$k] = $this->H[$i][$k] - $p; |
|
630
|
|
|
$this->H[$i][$k+1] = $this->H[$i][$k+1] - $p * $q; |
|
631
|
|
|
} |
|
632
|
|
|
// Accumulate transformations |
|
633
|
|
View Code Duplication |
for ($i = $low; $i <= $high; ++$i) { |
|
|
|
|
|
|
634
|
|
|
$p = $x * $this->V[$i][$k] + $y * $this->V[$i][$k+1]; |
|
635
|
|
|
if ($notlast) { |
|
636
|
|
|
$p = $p + $z * $this->V[$i][$k+2]; |
|
637
|
|
|
$this->V[$i][$k+2] = $this->V[$i][$k+2] - $p * $r; |
|
638
|
|
|
} |
|
639
|
|
|
$this->V[$i][$k] = $this->V[$i][$k] - $p; |
|
640
|
|
|
$this->V[$i][$k+1] = $this->V[$i][$k+1] - $p * $q; |
|
641
|
|
|
} |
|
642
|
|
|
} // ($s != 0) |
|
|
|
|
|
|
643
|
|
|
} // k loop |
|
644
|
|
|
} // check convergence |
|
645
|
|
|
} // while ($n >= $low) |
|
|
|
|
|
|
646
|
|
|
|
|
647
|
|
|
// Backsubstitute to find vectors of upper triangular form |
|
648
|
|
|
if ($norm == 0.0) { |
|
649
|
|
|
return; |
|
650
|
|
|
} |
|
651
|
|
|
|
|
652
|
|
|
for ($n = $nn-1; $n >= 0; --$n) { |
|
653
|
|
|
$p = $this->d[$n]; |
|
654
|
|
|
$q = $this->e[$n]; |
|
655
|
|
|
// Real vector |
|
656
|
|
|
if ($q == 0) { |
|
657
|
|
|
$l = $n; |
|
658
|
|
|
$this->H[$n][$n] = 1.0; |
|
659
|
|
|
for ($i = $n-1; $i >= 0; --$i) { |
|
660
|
|
|
$w = $this->H[$i][$i] - $p; |
|
661
|
|
|
$r = 0.0; |
|
662
|
|
|
for ($j = $l; $j <= $n; ++$j) { |
|
663
|
|
|
$r = $r + $this->H[$i][$j] * $this->H[$j][$n]; |
|
664
|
|
|
} |
|
665
|
|
|
if ($this->e[$i] < 0.0) { |
|
666
|
|
|
$z = $w; |
|
667
|
|
|
$s = $r; |
|
668
|
|
|
} else { |
|
669
|
|
|
$l = $i; |
|
670
|
|
|
if ($this->e[$i] == 0.0) { |
|
671
|
|
|
if ($w != 0.0) { |
|
672
|
|
|
$this->H[$i][$n] = -$r / $w; |
|
673
|
|
|
} else { |
|
674
|
|
|
$this->H[$i][$n] = -$r / ($eps * $norm); |
|
675
|
|
|
} |
|
676
|
|
|
// Solve real equations |
|
677
|
|
|
} else { |
|
678
|
|
|
$x = $this->H[$i][$i+1]; |
|
679
|
|
|
$y = $this->H[$i+1][$i]; |
|
680
|
|
|
$q = ($this->d[$i] - $p) * ($this->d[$i] - $p) + $this->e[$i] * $this->e[$i]; |
|
681
|
|
|
$t = ($x * $s - $z * $r) / $q; |
|
682
|
|
|
$this->H[$i][$n] = $t; |
|
683
|
|
|
if (abs($x) > abs($z)) { |
|
684
|
|
|
$this->H[$i+1][$n] = (-$r - $w * $t) / $x; |
|
685
|
|
|
} else { |
|
686
|
|
|
$this->H[$i+1][$n] = (-$s - $y * $t) / $z; |
|
687
|
|
|
} |
|
688
|
|
|
} |
|
689
|
|
|
// Overflow control |
|
690
|
|
|
$t = abs($this->H[$i][$n]); |
|
691
|
|
|
if (($eps * $t) * $t > 1) { |
|
692
|
|
View Code Duplication |
for ($j = $i; $j <= $n; ++$j) { |
|
|
|
|
|
|
693
|
|
|
$this->H[$j][$n] = $this->H[$j][$n] / $t; |
|
694
|
|
|
} |
|
695
|
|
|
} |
|
696
|
|
|
} |
|
697
|
|
|
} |
|
698
|
|
|
// Complex vector |
|
699
|
|
|
} elseif ($q < 0) { |
|
700
|
|
|
$l = $n-1; |
|
701
|
|
|
// Last vector component imaginary so matrix is triangular |
|
702
|
|
|
if (abs($this->H[$n][$n-1]) > abs($this->H[$n-1][$n])) { |
|
703
|
|
|
$this->H[$n-1][$n-1] = $q / $this->H[$n][$n-1]; |
|
704
|
|
|
$this->H[$n-1][$n] = -($this->H[$n][$n] - $p) / $this->H[$n][$n-1]; |
|
705
|
|
|
} else { |
|
706
|
|
|
$this->cdiv(0.0, -$this->H[$n-1][$n], $this->H[$n-1][$n-1] - $p, $q); |
|
707
|
|
|
$this->H[$n-1][$n-1] = $this->cdivr; |
|
708
|
|
|
$this->H[$n-1][$n] = $this->cdivi; |
|
709
|
|
|
} |
|
710
|
|
|
$this->H[$n][$n-1] = 0.0; |
|
711
|
|
|
$this->H[$n][$n] = 1.0; |
|
712
|
|
|
for ($i = $n-2; $i >= 0; --$i) { |
|
713
|
|
|
// double ra,sa,vr,vi; |
|
|
|
|
|
|
714
|
|
|
$ra = 0.0; |
|
715
|
|
|
$sa = 0.0; |
|
716
|
|
|
for ($j = $l; $j <= $n; ++$j) { |
|
717
|
|
|
$ra = $ra + $this->H[$i][$j] * $this->H[$j][$n-1]; |
|
718
|
|
|
$sa = $sa + $this->H[$i][$j] * $this->H[$j][$n]; |
|
719
|
|
|
} |
|
720
|
|
|
$w = $this->H[$i][$i] - $p; |
|
721
|
|
|
if ($this->e[$i] < 0.0) { |
|
722
|
|
|
$z = $w; |
|
723
|
|
|
$r = $ra; |
|
724
|
|
|
$s = $sa; |
|
725
|
|
|
} else { |
|
726
|
|
|
$l = $i; |
|
727
|
|
|
if ($this->e[$i] == 0) { |
|
728
|
|
|
$this->cdiv(-$ra, -$sa, $w, $q); |
|
729
|
|
|
$this->H[$i][$n-1] = $this->cdivr; |
|
730
|
|
|
$this->H[$i][$n] = $this->cdivi; |
|
731
|
|
|
} else { |
|
732
|
|
|
// Solve complex equations |
|
733
|
|
|
$x = $this->H[$i][$i+1]; |
|
734
|
|
|
$y = $this->H[$i+1][$i]; |
|
735
|
|
|
$vr = ($this->d[$i] - $p) * ($this->d[$i] - $p) + $this->e[$i] * $this->e[$i] - $q * $q; |
|
736
|
|
|
$vi = ($this->d[$i] - $p) * 2.0 * $q; |
|
737
|
|
|
if ($vr == 0.0 & $vi == 0.0) { |
|
|
|
|
|
|
738
|
|
|
$vr = $eps * $norm * (abs($w) + abs($q) + abs($x) + abs($y) + abs($z)); |
|
739
|
|
|
} |
|
740
|
|
|
$this->cdiv($x * $r - $z * $ra + $q * $sa, $x * $s - $z * $sa - $q * $ra, $vr, $vi); |
|
741
|
|
|
$this->H[$i][$n-1] = $this->cdivr; |
|
742
|
|
|
$this->H[$i][$n] = $this->cdivi; |
|
743
|
|
|
if (abs($x) > (abs($z) + abs($q))) { |
|
744
|
|
|
$this->H[$i+1][$n-1] = (-$ra - $w * $this->H[$i][$n-1] + $q * $this->H[$i][$n]) / $x; |
|
745
|
|
|
$this->H[$i+1][$n] = (-$sa - $w * $this->H[$i][$n] - $q * $this->H[$i][$n-1]) / $x; |
|
746
|
|
|
} else { |
|
747
|
|
|
$this->cdiv(-$r - $y * $this->H[$i][$n-1], -$s - $y * $this->H[$i][$n], $z, $q); |
|
748
|
|
|
$this->H[$i+1][$n-1] = $this->cdivr; |
|
749
|
|
|
$this->H[$i+1][$n] = $this->cdivi; |
|
750
|
|
|
} |
|
751
|
|
|
} |
|
752
|
|
|
// Overflow control |
|
753
|
|
|
$t = max(abs($this->H[$i][$n-1]), abs($this->H[$i][$n])); |
|
754
|
|
|
if (($eps * $t) * $t > 1) { |
|
755
|
|
|
for ($j = $i; $j <= $n; ++$j) { |
|
756
|
|
|
$this->H[$j][$n-1] = $this->H[$j][$n-1] / $t; |
|
757
|
|
|
$this->H[$j][$n] = $this->H[$j][$n] / $t; |
|
758
|
|
|
} |
|
759
|
|
|
} |
|
760
|
|
|
} // end else |
|
761
|
|
|
} // end for |
|
762
|
|
|
} // end else for complex case |
|
763
|
|
|
} // end for |
|
764
|
|
|
|
|
765
|
|
|
// Vectors of isolated roots |
|
766
|
|
|
for ($i = 0; $i < $nn; ++$i) { |
|
767
|
|
|
if ($i < $low | $i > $high) { |
|
768
|
|
|
for ($j = $i; $j < $nn; ++$j) { |
|
769
|
|
|
$this->V[$i][$j] = $this->H[$i][$j]; |
|
770
|
|
|
} |
|
771
|
|
|
} |
|
772
|
|
|
} |
|
773
|
|
|
|
|
774
|
|
|
// Back transformation to get eigenvectors of original matrix |
|
775
|
|
|
for ($j = $nn-1; $j >= $low; --$j) { |
|
776
|
|
|
for ($i = $low; $i <= $high; ++$i) { |
|
777
|
|
|
$z = 0.0; |
|
778
|
|
|
for ($k = $low; $k <= min($j, $high); ++$k) { |
|
779
|
|
|
$z = $z + $this->V[$i][$k] * $this->H[$k][$j]; |
|
780
|
|
|
} |
|
781
|
|
|
$this->V[$i][$j] = $z; |
|
782
|
|
|
} |
|
783
|
|
|
} |
|
784
|
|
|
} // end hqr2 |
|
785
|
|
|
|
|
786
|
|
|
|
|
787
|
|
|
/** |
|
788
|
|
|
* Constructor: Check for symmetry, then construct the eigenvalue decomposition |
|
789
|
|
|
* |
|
790
|
|
|
* @param array $Arg |
|
791
|
|
|
*/ |
|
792
|
|
|
public function __construct(array $Arg) |
|
793
|
|
|
{ |
|
794
|
|
|
$this->A = $Arg; |
|
|
|
|
|
|
795
|
|
|
$this->n = count($Arg[0]); |
|
796
|
|
|
|
|
797
|
|
|
$issymmetric = true; |
|
798
|
|
|
for ($j = 0; ($j < $this->n) & $issymmetric; ++$j) { |
|
799
|
|
View Code Duplication |
for ($i = 0; ($i < $this->n) & $issymmetric; ++$i) { |
|
|
|
|
|
|
800
|
|
|
$issymmetric = ($this->A[$i][$j] == $this->A[$j][$i]); |
|
801
|
|
|
} |
|
802
|
|
|
} |
|
803
|
|
|
|
|
804
|
|
|
if ($issymmetric) { |
|
805
|
|
|
$this->V = $this->A; |
|
806
|
|
|
// Tridiagonalize. |
|
807
|
|
|
$this->tred2(); |
|
808
|
|
|
// Diagonalize. |
|
809
|
|
|
$this->tql2(); |
|
810
|
|
|
} else { |
|
811
|
|
|
$this->H = $this->A; |
|
812
|
|
|
$this->ort = []; |
|
813
|
|
|
// Reduce to Hessenberg form. |
|
814
|
|
|
$this->orthes(); |
|
815
|
|
|
// Reduce Hessenberg to real Schur form. |
|
816
|
|
|
$this->hqr2(); |
|
817
|
|
|
} |
|
818
|
|
|
} |
|
819
|
|
|
|
|
820
|
|
|
/** |
|
821
|
|
|
* Return the eigenvector matrix |
|
822
|
|
|
* |
|
823
|
|
|
* @access public |
|
824
|
|
|
* @return array |
|
825
|
|
|
*/ |
|
826
|
|
|
public function getEigenvectors() |
|
827
|
|
|
{ |
|
828
|
|
|
$vectors = $this->V; |
|
829
|
|
|
|
|
830
|
|
|
// Always return the eigenvectors of length 1.0 |
|
831
|
|
|
$vectors = new Matrix($vectors); |
|
832
|
|
|
$vectors = array_map(function ($vect) { |
|
833
|
|
|
$sum = 0; |
|
834
|
|
View Code Duplication |
for ($i=0; $i<count($vect); $i++) { |
|
|
|
|
|
|
835
|
|
|
$sum += $vect[$i] ** 2; |
|
836
|
|
|
} |
|
837
|
|
|
$sum = sqrt($sum); |
|
838
|
|
View Code Duplication |
for ($i=0; $i<count($vect); $i++) { |
|
|
|
|
|
|
839
|
|
|
$vect[$i] /= $sum; |
|
840
|
|
|
} |
|
841
|
|
|
return $vect; |
|
842
|
|
|
}, $vectors->transpose()->toArray()); |
|
843
|
|
|
|
|
844
|
|
|
return $vectors; |
|
845
|
|
|
} |
|
846
|
|
|
|
|
847
|
|
|
|
|
848
|
|
|
/** |
|
849
|
|
|
* Return the real parts of the eigenvalues<br> |
|
850
|
|
|
* d = real(diag(D)); |
|
851
|
|
|
* |
|
852
|
|
|
* @return array |
|
853
|
|
|
*/ |
|
854
|
|
|
public function getRealEigenvalues() |
|
855
|
|
|
{ |
|
856
|
|
|
return $this->d; |
|
857
|
|
|
} |
|
858
|
|
|
|
|
859
|
|
|
|
|
860
|
|
|
/** |
|
861
|
|
|
* Return the imaginary parts of the eigenvalues <br> |
|
862
|
|
|
* d = imag(diag(D)) |
|
863
|
|
|
* |
|
864
|
|
|
* @return array |
|
865
|
|
|
*/ |
|
866
|
|
|
public function getImagEigenvalues() |
|
867
|
|
|
{ |
|
868
|
|
|
return $this->e; |
|
869
|
|
|
} |
|
870
|
|
|
|
|
871
|
|
|
|
|
872
|
|
|
/** |
|
873
|
|
|
* Return the block diagonal eigenvalue matrix |
|
874
|
|
|
* |
|
875
|
|
|
* @return array |
|
876
|
|
|
*/ |
|
877
|
|
|
public function getDiagonalEigenvalues() |
|
878
|
|
|
{ |
|
879
|
|
|
for ($i = 0; $i < $this->n; ++$i) { |
|
880
|
|
|
$D[$i] = array_fill(0, $this->n, 0.0); |
|
|
|
|
|
|
881
|
|
|
$D[$i][$i] = $this->d[$i]; |
|
|
|
|
|
|
882
|
|
|
if ($this->e[$i] == 0) { |
|
883
|
|
|
continue; |
|
884
|
|
|
} |
|
885
|
|
|
$o = ($this->e[$i] > 0) ? $i + 1 : $i - 1; |
|
886
|
|
|
$D[$i][$o] = $this->e[$i]; |
|
887
|
|
|
} |
|
888
|
|
|
return $D; |
|
889
|
|
|
} |
|
890
|
|
|
} // class EigenvalueDecomposition |
|
891
|
|
|
|
php-ai /
php-ml
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Duplicated code is one of the most pungent code smells. If you need to duplicate the same code in three or more different places, we strongly encourage you to look into extracting the code into a single class or operation.
You can also find more detailed suggestions in the “Code” section of your repository.